1. Field of the Invention
The present invention relates generally to imaging systems and, more particularly, to systems and methods for detecting regions, such as, e.g., kidney regions in renal dynamic studies.
2. Discussion of the Background
A variety of medical imaging systems are known. Some illustrative imaging systems include nuclear medical imaging systems (e.g., gamma cameras), computed tomography (CT or CAT) systems, magnetic resonance imaging (MRI) systems, positron-emission tomography (PET) systems, ultrasound systems and/or the like.
With respect to nuclear medical imaging systems, nuclear medicine is a unique medical specialty wherein radiation (e.g., gamma radiation) is used to acquire images that show, e.g., the function and/or anatomy of organs, bones and/or tissues of the body. Typically, radioactive compounds, called radiopharmaceuticals or tracers, are introduced into the body, either by injection or ingestion, and are attracted to specific organs, bones or tissues of interest. These radiopharmaceuticals produce gamma photon emissions that emanate from the body and are captured by a scintillation crystal, with which the photons interact to produce flashes of light or “events.” These events can be detected by, e.g., an array of photo-detectors, such as photomultiplier tubes, and their spatial locations or positions can be calculated and stored. In this manner, an image of an organ, tissue or the like under study can be created from the detection of the distribution of the radioisotopes in the body.
FIG. 1 depicts components of a typical nuclear medical imaging system 100 (i.e., having a gamma camera or a scintillation camera) which includes a gantry 102 supporting one or more detectors 108 enclosed within a metal housing and movably supported proximate a patient 106 located on a patient support (e.g., pallet) 104. Typically, the positions of the detectors 108 can be changed to a variety of orientations to obtain images of a patient's body from various directions. In many instances, a data acquisition console 200 (e.g., with a user interface and/or display) is located proximate a patient during use for a technologist 107 to manipulate during data acquisition. In addition to the data acquisition console 200, images are often developed via a processing computer system which is operated at another image processing computer console including, e.g., an operator interface and a display, which may often be located in another room, to develop images. By way of example, the image acquisition data may, in some instances, be transmitted to the processing computer system after acquisition using the acquisition console.
In, for example, nuclear medical imaging, as well as in other types of imaging, dynamic studies are often employed. For example, dynamic studies may involve a study in which the temporal distribution of a radioactive tracer is analyzed. Of the various methods for performing dynamic studies of gamma camera and the like images, principal component analyses and factor analyses have been employed. See e.g. The Use of Principal Components in the Quantitative Analysis of Gamma Camera Dynamic Studies, D. C. Barber, Phys. Med. Biol. 25 No 2 (March 1980) 283-292, incorporated by reference below (“The reduction of the enormous quantity of data in a radionuclide dynamic study to a few diagnostic parameters presents a problem. Conventional methods of data reduction using regions-of-interest or functional images have several defects which potentially limit their usefulness. Using a principal components analysis of the elemental curves representing the change of activity with time in each pixel, followed by a further factor analysis, it is possible to extract the fundamental functional changes of activity which underlie the observed variation of activity. An example of this analysis on a dynamic brain scan suggests that the three fundamental phases of activity represent activity in the arterial system, the venous system and diffusion of tracer into the tissues.”)
A background discussion of factor analysis of dynamic studies (FADS) is presented in the following article Background Assessment of brain perfusion using parametric and factor images extracted from dynamic contrast-enhanced MR images of A. L. Martel and A. R. Moody, University of Nottingham, as incorporated herein-below:                “Factor analysis is a useful technique for extracting information from a dynamic study without making any a priori assumptions about physiology. In the factor model it is assumed both that the correlations between a set of observed variables can be explained in terms of a set of latent variables and that the number of latent variables present in a data set is less than the number of observed variables. These latent variables or factors will often describe some physical property of the system being observed. Alternatively, they may be theoretical constructs which have no physiological significance but which simplify the task of interpreting the data. The dynamic study can be represented by the (T×N) matrix D, where T is the number of frames in the study and N is the number of pixels in each frame. Each row of the matrix represents an image in the sequence and each column represents a pixel signal intensity curve. If M is equal to the number of kinetic compartments present in the data, F is the (T×M) column matrix of factor curves, A is the (M×N) matrix of factor images and E is the matrix of unique factors then the dynamic study can be represented by the equationD=FA+E  (1)        The movement of the contrast agent through a compartment is assumed to be homogeneous, i.e. the time course of tracer through a compartment should be spatially invariant. If this assumption is true and if sufficient factors have been identified then the variance represented by the matrix E is due to random noise only. In practice inhomogeneities do exist but we assume that these account for a very small proportion of the total variance.        Since there are an infinite number of possible solutions to equation (1) it is necessary to apply constraints in order to obtain a unique solution. Principal Components Analysis (PCA) uses a statistical constraint to obtain a unique set of orthogonal factors with no a priori assumptions being made about the data. The PC curves are obtained by extracting the eigenvectors of the covariance matrix C (given by DD±) in decreasing order of importance, i.e. Ii>Ii+1 where Ii is the eigenvalue corresponding to the i'th eigenvector ui. Since the first PC accounts for most of the information in the study, with subsequent PCs containing progressively less, it can be assumed that there exists a subset of M PCs which account for all of the useful information, with the remaining (T−M) PCs representing pure noise. The dynamic study can therefore be represented by        
                    D        =                                            ∑                              i                =                1                            M                        ⁢                                          λ                i                            ⁢                                                u                  _                                i                            ⁢                                                v                  _                                i                t                                              +                                    ∑                              i                =                                  M                  +                  1                                            r                        ⁢                                          λ                i                            ⁢                                                u                  _                                i                            ⁢                                                v                  _                                i                t                                                                        (        2        )                             where ui, is the (T element) i'th principal component curve and vi is the corresponding (N element) vector of coefficients.        Occasionally it is possible to obtain useful information from the orthogonal PCs, for example an area of increased or decreased perfusion may appear as a focal anomaly on one or more of the PC images. Physiological studies, however, are often more usefully represented by oblique factors. The problem of identifying a set of physiologically meaningful oblique factors can be simplified by extracting the first M principal components in order to reduce the dimensionality of the data set, and then rotating these components under the control of certain constraints. Substituting for D from equation (2) in equation (1) and ignoring the residual matrix E givesFA=U*√{square root over (R*)}V*  (3)         where U* is the (T×M) orthonormal column matrix containing the first M eigenvectors, V* is the (M×N) row matrix of coefficients and R* is the diagonal matrix of eigenvalues. F and A can therefore be represented byF=(U*√{square root over (R*)})T  (4a)and A=T−1V  (4b)         where T is an (M×M) rotation matrix and TT−1=I. Much of the work on FADS has been carried out on dynamic nuclear medicine studies and various constraints have been proposed. The one most commonly used is the positivity constraint [1] which assumes that neither the factor images nor the factor curves should contain any negative values. This constraint has been applied to various types of nuclear medicine studies [2,3] and more recently to contrast enhanced MRI studies [4,5] with some success. The positivity constraint is not sufficient on its own to produce a unique solution [6] and the use of additional constraints has been investigated [7,8,9]. We have used a modified version of the apex-seeking technique [1] which incorporates the following constraints appropriate for dynamic contrast enhanced MRI studies:                    Positivity constraint. Since the signal intensity increases in proportion to the concentration of contrast agent for T1 weighted images, there should be no negative values in either F or A.            Uniform background. We assume that the first factor to be extracted will correspond to a non-enhancing background factor. The factor curve corresponding to the background is obtained by projecting a uniform signal intensity curve onto the subspace defined by the matrix U*. This allows for any fluctuations in background signal intensity during the study to be taken into account.            Zeroes constraint. All of the non-background factor curves extracted from the data will have zero amplitude for the first 15 seconds, as the contrast is not administered until the 5'th image.                        In most cases three factors can be extracted from the data: a background or non-enhancing factor, an early vascular factor which is strongly correlated to arterial flow, and a late vascular factor which is strongly correlated to venous flow. In this way, functional images characterizing brain perfusion can be obtained without imposing any model upon the data.”        
A number of illustrative background systems and methods are shown in the following U.S. patents, the entire disclosures of which are incorporated herein by reference:                1. U.S. Pat. No. 5,634,469, entitled Method for Localizing a Site of Origin of Electrical Heart Activity, issued on Jun. 3, 1997, listed as assigned to Siemens Aktiengesellschaft.        2. U.S. Pat. No. 5,887,074, entitled Local Principal Component Based Method for Detecting Activation Signals In Functional MR Images, issued on Mar. 23, 1999, listed as assigned to Siemens Corporate Research, Inc.        
In addition, a number of further illustrative background systems and methods are shown in the following publications, the entire disclosures of which are incorporated herein by reference:                1. The use of principal components in the quantitative analysis of gamma camera dynamic studies, D C Barber, Phys. Med. Biol. 25 No 2 (March 1980) 283-292.        2. Towards automatic analysis of dynamic radionuclide studies using principal-components factor analysis, K S Nijran and D C Barber 1985 Phys. Med. Biol. 30 1315-1325 (“A method is proposed fpr automatic analysis of dynamic radionuclide studies using the mathematical technique of principal-components factor analysis. This method is considered as a possible alternative to the conventional manual regions-of-interest method widely used. The method emphasizes the importance of introducing a priori information into the analysis about the physiology of at least one of the functional structures in a study. Information is added by using suitable mathematical models to describe the underlying physiological processes. A single physiological factor is extracted representing the particular dynamic structure of interest. Two spaces ‘study space, S’ and ‘theory space, T’ are defined in the formation of the concept of intersection of spaces. A one-dimensional intersection space is computed. An example from a dynamic 99T cm DTPA kidney study is used to demonstrate the principle inherent in the method proposed. The method requires no correction for the blood background activity, necessary when processing by the manual method. The careful isolation of the kidney by means of region of interest is not required. The method is therefore less prone to operator influence and can be automated.”)        3. A quantitative comparison of some FADS methods in renal dynamic studies using simulated and phantom data, A S Houston and W F D Sampson, Phys. Med. Biol. 42 No 1 (January 1997) 199-217.        4. Rotation to simple structure in factor analysis of dynamic radionuclide studies, M Samal, M Karny, H Surova, E Marikova and Z Dienstbier, Phys. Med. Biol. 32 No 3 (March 1987) 371-382.        5. Factor analysis of dynamic function studies using a priori Physiological information (nuclear medicine), K S Nijran and D C Barber, Phys. Med. Biol. 31 No 10 (October 1986) 1107-1117.        6. The determination of the number of statistically significant factors in factor analysis of dynamic structures, P Hannequin, J C Liehn and J Valeyre, Phys. Med. Biol. 34 No 9 (September 1989) 1213-1227.        7. On the existence of an unambiguous solution in factor analysis of dynamic studies, M Samal, M Karny, H Surova, P Penicka, E Marikova and Z Dienstbier, Phys. Med. Biol. 34 No 2 (February 1989) 223-228.        8. Background correction in factor analysis of dynamic scintigraphic studies: necessity and implementation, M Van Daele, J Joosten, P Devos, A Vandecruys, J L Willems and M De Roo, Phys. Med. Biol. 35 No 11 (November 1990) 1477-1485.        9. A statistical model for the determination of the optimal metric in factor analysis of medical image sequences (FAMIS), H Benali, I Buvat, F Frouin, J P Bazin and R Di Paola, Phys. Med. Biol. 38 No 8 (August 1993) 1065-1080.        10. Linear dimension reduction of sequences of medical images: III. Factor analysis in signal space, Flemming Hermansen and Adriaan A Lammertsma, Phys. Med. Biol. 41 No 8 (August 1996) 1469-1481.        11. A control systems approach for the simulation of renal dynamic software phantoms for nuclear medicine, Alexander S Houston, William F D Sampson, Romina M J Jose and James F Boyce Phys. Med. Biol. 44 No 2 (February 1999) 401-411.        12. Statistical distribution of factors and factor images in factor analysis of medical image sequences, I Buvat, H Benali and R Di Paola, Phys. Med. Biol. 43 No 6 (June 1998) 1695-1711.        13. Factor analysis with a priori knowledge—application in dynamic cardiac SPECT, A Sitek, E V R Di Bella and G T Gullberg, Phys. Med. Biol. 45 No 9 (1 Sep. 2000) 2619-2638.        14. Assessment of brain perfusion using parametric and factor images extracted from dynamic contrast-enhanced MR images, A. L. Martel and A. R. Moody, University of Nottingham (quoted above).        
While a variety of systems and methods related to dynamic analyses exist, there remains a need for, inter alia, improved systems and methods that enable, e.g., automatic detection of regions, especially, e.g., kidney regions, in dynamic studies. For example, the detection of the cortical region of the kidneys is an important part in the evaluation of renal functions. However, existing automated methods do not work well. In fact, this is typically performed manually, but the manual method is time consuming and requires significant expertise of the operator. While, as described above, principal component analyses and factor analyses are well known techniques and have been implemented in some background environments, there are no existing techniques that accurately and automatically determine, e.g., the cortical region of the kidneys.
Thus, while a variety of systems and methods are known, there remains a continued need for improved systems and methods overcoming the above and/or other problems with existing systems and methods.